SoroushVahidi/scidocs huggingface dataset:
Classroom Re-design to Facilitate Student Learning : A Case Study of Changes to a University Classroom
(2016)

This case study examines the physical aspects of a particular university classroom, and what affect specific changes to the classroom had on the perceptions of students, instructors and observers regarding the room as an effective learning space. We compare survey and focus group data collected from students taking courses in the classroom prior to changes to the physical environment with comparable data from students taking courses in the same classroom after specific changes had been made. Immediately following changes to the classroom, notable increases were observed in reported perceptions of student satisfaction with the physical environment, including perceptions of the classroom as a more effective and engaging learning space. Similar perceptions of improvement as a teaching-learning space were reported by instructors and observers. However, subsequent follow-up data collection and analyses suggested little if any sustained increase in perceptions of efficacy of the room as a learning space; indeed, most reported variables returned to baseline levels. The implications of these findings and their relevance to classroom design nevertheless may provide insight regarding the manner in which physical space might support or even enhance teaching and learning. Keywords: learning spaces, active learning, classrooms, teaching and learning environment, classroom design For a number of years there has been an on-going pedagogical shift in higher education away from a traditional content delivery model of instruction to more active models of learning in which students play more involved and interactive roles within the classroom (Cornell, 2002; Brown, 2006). This movement has been coupled with the recognition that the traditional university classroom, with its unidirectional design and tiered, fixed theatre-like seating, is insufficient to accommodate what have increasingly become more varied teaching and learning practices. This growing realization that as the nature of teaching and learning evolves so too must teaching and learning spaces has, in recent years, resulted in a heightened interest among scholars in the examination of classroom space and, more specifically, the inquiry into the connection between classroom design and pedagogy and learning (Brooks, 2011). This study presents findings from an on-going research project examining classroom spaces on a small university campus. The present case study, which is one component of the larger project, compares perceptions of a specific classroom as an effective learning space before and after relatively extensive changes were made to the physical environment of the room. These changes were guided by feedback from students and instructors as well as by existing literature on innovative classroom designs. In particular, we were interested in assessing if these changes 1	  University	  of	  Lethbridge,	  Lethbridge,	  Alberta,	  Canada
Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models
(2007)

The Kullback Leibler (KL) divergence is a widely used tool in statistics and pattern recognition. The KL divergence between two Gaussian mixture models (GMMs) is frequently needed in the fields of speech and image recognition. Unfortunately the KL divergence between two GMMs is not analytically tractable, nor does any efficient computational algorithm exist. Some techniques cope with this problem by replacing the KL divergence with other functions that can be computed efficiently. We introduce two new methods, the variational approximation and the variational upper bound, and compare them to existing methods. We discuss seven different techniques in total and weigh the benefits of each one against the others. To conclude we evaluate the performance of each one through numerical experiments.
Interprocedural Semantic Change-Impact Analysis using Equivalence Relations
(2016)

Change-impact analysis (CIA) is the task of determining the set of program elements impacted by a program change. Precise CIA has great potential to avoid expensive t sting and code reviews for (parts of) changes that are refactoring s (semantics-preserving). Existing CIA is imprecise becaus e it is coarse-grained, deals with only few refactoring patterns,or is unaware of the change semantics. We formalize the notion of change impact in terms of the trace semantics of two program versions. We show how to leverage equivalence relations to make dataflow-based CIA aware of th e change semantics, thereby improving precision in the prese nce of semantics-preserving changes. We propose an anytime algorithm that allows applying costly equivalence relation inference incrementally to refine the set of impacted statements. We ha ve implemented a prototype in SYM DIFF , and evaluated it on 322 real-world changes from open-source projects and benchmar k programs used by prior research. The evaluation results sho w an average 35% improvement in the size of the set of impacted statements compared to standard dataflow-based techniques .
Dual Learning based Multi-Objective Pairwise Ranking
(2018)

There are many different recommendation tasks in our real life. The item ranking task is ranking a set of items based on users’ preferences. The user ranking task is referred to as user retrieval, find the potential users and recommend items to them. We find every recommendation task has it’s own dual task, e.g., Recommend an item to a user versus the user is supposed to become the item’s potential interested user. In this paper, we propose a novel dual learning based ranking framework with the overall aim of learning users’ preferences over items and learning items’ preferences over users by minimizing a pairwise ranking loss. We generate effective feedback signal from close loop formed by dual task. Through a reinforcement learning process, we can iteratively update these two tasks’ models to catch relations between the item recommendation task and the user retrieval task. The experiments show that our method outperforms classical approaches for multi-object recommendation tasks, especially for user retrieval tasks.
Business Analytics in (a) Blink
(2012)

The Blink project’s ambitious goal is to answer all Business Intelligence (BI) queries in mere seconds, regardless of the database size, with an extremely low total cost of ownership. Blink is a new DBMS aimed primarily at read-mostly BI query processing that exploits scale-out of commodity multi-core processors and cheap DRAM to retain a (copy of a) data mart completely in main memory. Additionally, it exploits proprietary compression technology and cache-conscious algorithms that reduce memory bandwidth consumption and allow most SQL query processing to be performed on the compressed data. Blink always scans (portions of) the data mart in parallel on all nodes, without using any indexes or materialized views, and without any query optimizer to choose among them. The Blink technology has thus far been incorporated into two IBM accelerator products generally available since March 2011. We are now working on the next generation of Blink, which will significantly expand the “sweet spot” of the Blink technology to much larger, disk-based warehouses and allow Blink to “own” the data, rather
Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands.
(2016)

The term 'synergy' - from the Greek synergia - means 'working together'. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project "The Hand Embodied" (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.
The thalamic dynamic core theory of conscious experience
(2011)

I propose that primary conscious awareness arises from synchronized activity in dendrites of neurons in dorsal thalamic nuclei, mediated particularly by inhibitory interactions with thalamic reticular neurons. In support, I offer four evidential pillars: (1) consciousness is restricted to the results of cortical computations; (2) thalamus is the common locus of action of brain injury in vegetative state and of general anesthetics; (3) the anatomy and physiology of the thalamus imply a central role in consciousness; (4) neural synchronization is a neural correlate of consciousness.
PFC Cuk Converter-Fed BLDC Motor Drive
(2015)

This paper deals with a power factor correction (PFC)-based Cuk converter-fed brushless dc motor (BLDC) drive as a cost-effective solution for low-power applications. The speed of the BLDC motor is controlled by varying the dc-bus voltage of a voltage source inverter (VSI) which uses a low frequency switching of VSI (electronic commutation of the BLDC motor) for low switching losses. A diode bridge rectifier followed by a Cuk converter working in a discontinuous conduction mode (DCM) is used for control of dc-link voltage with unity power factor at ac mains. Performance of the PFC Cuk converter is evaluated under four different operating conditions of discontinuous and continuous conduction modes (CCM) and a comparison is made to select a best suited mode of operation. The performance of the proposed system is simulated in a MATLAB/Simulink environment and a hardware prototype of the proposed drive is developed to validate its performance over a wide range of speed with unity power factor at ac mains.
Pokémon Go: Impact on Yelp Restaurant Reviews
(2017)

Pokémon Go, the popular Augmented Reality based mobile application, launched in July of 2016. The game's meteoric rise in usage since that time has had an impact on not just the mobile gaming industry, but also the physical activity of players, where they travel, where they spend their money, and possibly how they interact with other social media applications. In this paper, we studied the impact of Pokémon Go on Yelp reviews. For restaurants near PokéStops, we found a slight drop in the number of online reviews.
CIVSched: A Communication-Aware Inter-VM Scheduling Technique for Decreased Network Latency between Co-Located VMs
(2014)

Server consolidation in cloud computing environments makes it possible for multiple servers or desktops to run on a single physical server for high resource utilization, low cost, and reduced energy consumption. However, the scheduler in the virtual machine monitor (VMM), such as Xen credit scheduler, is agnostic about the communication behavior between the guest operating systems (OS). The aforementioned behavior leads to increased network communication latency in consolidated environments. In particular, the CPU resources management has a critical impact on the network latency between co-located virtual machines (VMs) when there are CPUand I/O-intensive workloads running simultaneously. This paper presents the design and implementation of a communication-aware inter-VM scheduling (CIVSched) technique that takes into account the communication behavior between inter-VMs running on the same virtualization platform. The CIVSched technique inspects the network packets transmitted between local co-resident domains to identify the target VM and process that will receive the packets. Thereafter, the target VM and process are preferentially scheduled by the VMM and the guest OS. The cooperation of these two schedulers makes the network packets to be timely received by the target application. Experimental results on the Xen virtualization platform depict that the CIVSched technique can reduce the average response time of network traffic by approximately 19 percent for the highly consolidated environment, while keeping the inherent fairness of the VMM scheduler.
Classification of plant leaf images with complicated background
(2008)

Keywords: Image segmentation Plant leaf Complicated background Watershed segmentation Hu geometric moments Zernike moment Moving center hypersphere (MCH) classifier a b s t r a c t Classifying plant leaves has so far been an important and difficult task, especially for leaves with complicated background where some interferents and overlapping phenomena may exist. In this paper, an efficient classification framework for leaf images with complicated background is proposed. First, a so-called automatic marker-controlled watershed segmen-tation method combined with pre-segmentation and morphological operation is introduced to segment leaf images with complicated background based on the prior shape information. Then, seven Hu geometric moments and sixteen Zernike moments are extracted as shape features from segmented binary images after leafstalk removal. In addition , a moving center hypersphere (MCH) classifier which can efficiently compress feature data is designed to address obtained mass high-dimensional shape features. Finally, experimental results on some practical plant leaves show that proposed classification framework works well while classifying leaf images with complicated background. There are twenty classes of practical plant leaves successfully classified and the average correct classification rate is up to 92.6%. Plants play the most important part in the cycle of nature. They are the primary producers that sustain all other life forms including people. This is because plants are the only organisms that can convert light energy from the sun into food. Animals, incapable of making their own food, depend directly or indirectly on plants for their supply of food. Moreover, all of the oxygen available for living organisms comes from plants. Plants are also the primary habitat for thousands of other organisms. In addition, many of the fuel people use today, such as coal, natural gas and gasoline, were made from plants that lived millions of years ago. However, in recent years, people have been seriously destroying the natural environments, so that many plants constantly die and even die out every year. Conversely, the resulting ecological crisis has brought many serious consequences including land desertion, climate anomaly, land flood, and so on, which have menaced the survival of human being and the development of society. Now, people have realized the importance and urgency of protecting plant resource. Besides taking effective measures to protect plants, it is important for ordinary people to know or classify plants which can further enhance the public's consciousness of plant protection. Therefore, besides professional botanists, many non-professional researchers have paid more …
How do I communicate my emotions on SNS and IMs?
(2016)

With influx of new devices and applications over the past few years, computer mediated communication has developed as an alternate to face-to-face communication. Through our study we attempt to understand how individuals communicate in mediated settings in emotion-laden situations. We explore their preferred medium of communication in different situations along with analysis of the content of communication. Spatial arrays, lexical surrogates, vocal spellings and grammatical markers were used as strategies by individuals for communicating non-verbal cues in text based communication. We also look at how messages sent on IMs or posts on social networks are interpreted by readers in terms of the emotional state of the sender. We found that while valence of the sender gets easily and accurately communicated, arousal is misinterpreted in most situations. In this paper, we present findings from our study which can be valuable for technology companies looking to better the current communication experience across different media.
Handling Multiword Expressions in Causality Estimation
(2017)

Previous studies on causality estimation mainly aquire causal event pairs from a large corpus based on lexico-syntactic patterns and coreference relations, and estimate causality by a statistical method. However, most of the previous studies assume event pairs can be represented by a pair of single words, therefore they cannot estimate multiword causality correctly (e.g.“tired”-“give up”) . In this paper, we create a list of multiword expressions and extend an existing method. Our evaluation demonstrates that the proper treatment of multiword expression events is effective and the proposed method outperforms the state-of-the-art causality estimation model.
Health-Related Disaster Communication and Social Media: Mixed-Method Systematic Review.
(2018)

This mixed-method evidence synthesis drew on Cochrane methods and principles to systematically review literature published between 2003 and 2016 on the best social media practices to promote health protection and dispel misinformation during disasters. Seventy-nine studies employing quantitative, qualitative, and mixed methods on risk communication during disasters in all UN-languages were reviewed, finding that agencies need to contextualize the use of social media for particular populations and crises. Social media are tools that still have not become routine practices in many governmental agencies regarding public health in the countries studied. Social media, especially Twitter and Facebook (and equivalents in countries such as China), need to be incorporated into daily operations of governmental agencies and implementing partners to build familiarity with them before health-related crises happen. This was especially observed in U.S. agencies, local government, and first responders but also for city governments and school administrations in Europe. For those that do use social media during health-related risk communication, studies find that public relations officers, governmental agencies, and the general public have used social media successfully to spread truthful information and to verify information to dispel rumors during disasters. Few studies focused on the recovery and preparation phases and on countries in the Southern hemisphere, except for Australia. The vast majority of studies did not analyze the demographics of social media users beyond their geographic location, their status of being inside/outside the disaster zone; and their frequency and content of posting. Socioeconomic demographics were not collected and/or analyzed to drill deeper into the implications of using social media to reach vulnerable populations. Who exactly is reached via social media campaigns and who needs to be reached with other means has remained an understudied area.
Don't fall into a trap: Physical side-channel analysis of ChaCha20-Poly1305
(2017)

The stream cipher ChaCha20 and the MAC function Poly1305 have been published as IETF RFC 7539. Since then, the industry is starting to use it more often. For example, it has been implemented by Google in their Chrome browser for TLS and also support has been added to OpenSSL, as well as OpenSSH. It is often claimed, that the algorithms are designed to be resistant to side-channel attacks. However, this is only true, if the only observable side-channel is the timing behavior. In this paper, we show that ChaCha20 is susceptible to power and EM side-channel analysis, which also translates to an attack on Poly1305, if used together with ChaCha20 for key generation. As a first countermeasure, we analyze the effectiveness of randomly shuffling the operations of the ChaCha round function.
Depressive realism: a meta-analytic review.
(2012)

The current investigation represents the first meta-analysis of the depressive realism literature. A search of this literature revealed 75 relevant studies representing 7305 participants from across the US and Canada, as well as from England, Spain, and Israel. Results generally indicated a small overall depressive realism effect (Cohen's d=-.07). Overall, however, both dysphoric/depressed individuals (d=.14) and nondysphoric/nondepressed individuals evidenced a substantial positive bias (d=.29), with this bias being larger in nondysphoric/nondepressed individuals. Examination of potential moderator variables indicated that studies lacking an objective standard of reality (d=-.15 versus -.03, for studies possessing such a standard) and that utilize self-report measures to measure symptoms of depression (d=.16 versus -.04, for studies which utilize structured interviews) were more likely to find depressive realism effects. Methodological paradigm was also found to influence whether results consistent with depressive realism were found (d's ranged from -.09 to .14).
Pattern Reconfigurable Antenna Based on Morphing Bistable Composite Laminates
(2017)

In this paper, a novel pattern reconfigurable antenna based on morphing bistable composite laminates is presented. The bistable asymmetric glass-fiber reinforced polymer (GFRP) composite laminates have two stable configurations with curvatures of opposite signs. The antenna pattern is reconfigured by transforming the configuration of the bistable GFRP laminate which acts as the substrate of the antenna. The coplanar waveguide transmission lines feeding technique is used for the microstrip quasi-Yagi antenna. A prototype of the proposed antenna is fabricated using a semi-automatic screen printer and an autoclave. The transformation between the two stable states of the proposed antenna using Ni/Ti shape memory alloy springs is investigated experimentally. The out-of-plane displacements, reflection coefficients and radiation patterns for the two stable configurations of the antenna are measured, which agree well with the simulated results. The main beam direction is 89° and 59° for the two stable configurations, respectively. In addition, the influences of various bending radii on the radiation patterns are investigated to gain a thorough understanding of the reconfigurable mechanism of the proposed antenna. Finally, a two-element array of such an antenna is presented and measured. The proposed antenna provides a potential application in multifunctional, conformal, morphing, and integrated structures.